Bayesian classification and unsupervised learning for isolating weeds in row crops
Tóm tắt
This paper presents a weed/crop classification method using computer vision and morphological analysis. Subsequent supervised and unsupervised learning methods are applied to extract dominant morphological characteristics of weeds present in corn and soybean fields. The novelty of the presented technique resides in the feature extraction process that is based on spatial localization of vegetation in fields. Features from the weed leaf area distribution are extracted from the cultivation inter-rows, then features from the crop are inferred from the mixture model equation. Those extracted features are then passed to a naive bayesian classifier and a gaussian mixture clustering algorithm to discriminate weed from crop plant. The presented technique correctly classifies an average of 94 % of corn and soybean plants and 85 % of the weed (multiple species) without any prior knowledge on the species present in the field.
Tài liệu tham khảo
Aitkenhead M.J., Dalgetty I.A., Mullins C.E., McDonald A.J.S., Strachan N.J.C. (2003) Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture 39(3):157–171
Alpaydin E (2010) Introduction to machine learning, 2nd edn. MIT Press, USA
Asif M, Amir S, Israr A, Faraz M (2010) A vision system for autonomous weed detection robot. Int J Comput Electr Eng 2(3):486–491
Bowman A, Azzalini A (1997) Applied smoothing techniques for data analysis. Clarendon Press, Oxford
DeLorenzo M.E., Scott G.I., Ross P.E. (2001) Toxicity of pesticides to aquatic microorganisms: a review. Environmental Toxicology and Chemistry 20(1):84–98
Dempster A.P., Laird N.M., Rubin D.B. (1977) Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological) 39:1–38
Figueiredo M.A.T., Jain A.K. (2002) Unsupervised learning of finite mixture models. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(3):381–396
Freemark K., Boutin C. (1995) Impacts of agricultural herbicide use on terrestrial wildlife in temperate landscapes: A review with special reference to north america. Agriculture, Ecosystems & Environment 52(2):67–91
Hemming J., Rath T. (2001) Precision Agriculture Computer-Vision-based Weed Identification under Field Conditions using Controlled Lighting. Journal of Agricultural Engineering Research 78(3):233–243
Hough P.V.C. (1962) A Method and Means for Recognizing Complex Patterns. U.S. Patent 3,069,654
Jones G., Gée C., Truchetet F. (2009) Assessment of an inter-row weed infestation rate on simulated agronomic images. Computers and Electronics in Agriculture 67(1-2):43–50
Longchamps L., Panneton B., Samson G., Leroux G., Thériault R. (2010) Discrimination of corn, grasses and dicot weeds by their uv-induced fluorescence spectral signature. Precision Agric 11:181–197
Ngouajio M., Lemieux C., Fortier J.J., Careau D., Leroux G.D. (1998) Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technol 12(3):446–453
Otsu N. (1979) A threshold selection method from gray-level histograms. IEEE Transaction on Systems, Man, and Cybernetics 9(1):62–66
Panneton B (2009) Initiative des stratégies de réduction des risques liés aux pesticides. Rapport annuel PRR07–10, Centre pour la lutte antiparasitaire-Agriculture et Agroalimentaire Canada
Panneton B., Brouillard M. (2009) Colour representation methods for segmentation of vegetation in photographs. Biosyst Eng 102:365–378
Panneton B. Simard MJ, Leroux GD, Légére A (2010) Mise au point et impact sur la distribution spatio-temporelle des adventices d’un système d’aide ála décision pour l’application des herbicides en maïs-soya. Rapport final PRR07–10, Centre pour la lutte antiparasitaire-Agriculture et Agroalimentaire Canada
Parzen E. (1962) On estimation of a probability density function and mode. Annals of Mathematical Statistics 33:1065–1076
Rosenblatt M. (1956) Remarks on some nonparametric estimates of a density function. Annals of Mathematical Statistics 27:832–837
Slaughter D.C., Giles D.K., Downey D. (2008) Autonomous robotic weed control systems: A review.Computers and Electronics in Agriculture 61(1):63–78 doi:10.1016/j.compag.2007.05.008
Stafford J.V. (2000) Implementing precision agriculture in the 21th century. J Agric Eng Res 76:267–275
Tellaeche A., Pajares G., Burgos-Artizzu X.P., Ribeiro A. (2011) A computer vision approach for weeds identification through Support Vector Machines. Applied Soft Computing 11(1):908–915
Timmermann C., Gerhards R., Kuhbauch W. (2003) The economic impact of site-specific weed control. Precision Agric 4(3):249–260
Yang C.C., Prasher S.O., Landry J.A., Ramaswamy H.S. (2003) Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precision Agric 4(1):5–18
Åstrand B., Baerveldt A.J. (2005) A vision based row-following system for agricultural field machinery. Mechatronics 15(2):251–269